Comparison between confocal scanning laser tomography, scanning laser polarimetry and optical coherence tomography on the ability to detect localised retinal nerve fibre layer defects in glaucoma patients
Bibliographic record
Abstract
BACKGROUND/AIM: To compare the ability of confocal scanning laser tomography (CSLT), scanning laser polarimetry (SLP) and optical coherence tomography (OCT) in recognising localised retinal nerve fibre layer (RNFL) defects. METHODS: 51 eyes from 43 patients with glaucoma were identified by two observers as having RNFL defects visible on optic disc photographs. 51 eyes of 32 normal subjects were used as controls. Three masked observers evaluated CSLT, SLP and OCT images to determine subjectively the presence of localised RNFL defects. RESULTS: Interobserver agreement was highest with OCT, followed by SLP and CSLT (mean kappa: 0.83, 0.69 and 0.64, respectively). RNFL defects were identified in 58.8% of CSLT, 66.7% of SLP and 54.9% of OCT (p = 0.02 between SLP and OCT) by at least two observers. In the controls, 94.1% of CSLT, 84.3% of SLP and 94.1% of OCT scans, respectively, were rated as normal (p = 0.02 between CSLT and SLP, and SLP and OCT). CONCLUSION: Approximately 20-40% of localised RNFL defects identified by colour optic disc photographs are not detected by CSLT, SPL or OCT. SLP showed a higher number of false-positive results than the other techniques, but also had a higher proportion of correctly identified RNFL defects in the glaucoma population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".